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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The Computational Neuroscience certification course explores fundamental queries on learning, memory, and decision-making by quantifying neuronal activity and modeling spiking. Students delve into how neurons represent information and its practical applications, culminating in computational models elucidating plasticity's role in learning, memory, and cognition.

Catering to Neural/Cognitive Sciences and AI enthusiasts, the Computational Neuroscience certification by Swayam offers a deep dive into Biological Sciences and Computational Biology. Emphasizing the industry's AI support, it equips students with insights into neural computation's real-world implications, preparing them for multifaceted challenges in understanding brain mechanisms and AI advancements.

Also Read: Online Neural Networks Certification Courses

The Highlights

Elective Online Course
12 Weeks Duration
Offered by IIT Kharagpur

Programme Offerings

Certificate of completion
Practical Learning
Hands-on Experience

Courses and Certificate Fees

Fees Informations	Certificate Availability	Certificate Providing Authority
INR 1000	yes	IIT Kharagpur

The Computational Neuroscience certification fees is free. However, if you want a certificate, you must register and take the proctored exam at designated centres, which is optional and comes with a fee of Rs 1000.

Computational Neuroscience Certification Fee Structure

Particulars	Total Fees
Computational Neuroscience (exam)	Rs 1000/-

Eligibility Criteria

Academic Qualifications

You need to have completed first-year college courses in Mathematics and Biology before taking the Computational Neuroscience certification course.

Certification Qualifying Details

To receive the Computational Neuroscience certification by Swayam, you need a minimum average assignment score of 10/25 and an exam score of 30/75.

What you will learn

After completing the Computational Neuroscience certification syllabus, you will be able to understand the intricate processes of information handling within the brain's neurons. You will learn how to quantify neuronal activity and model spiking in individual neurons, laying the groundwork for a nuanced comprehension of neural computation.

Upon completion of the Computational Neuroscience training, you will gain insights into the critical concept of plasticity, a cornerstone of the brain's functionality, impacting learning, memory, and cognition. You will also be equipped with computational modelling skills, allowing you to implement and analyse plasticity in the context of neural processes.

Who it is for

AI DeveloperNeurologistMachine Learning EngineerResearch ScientistNeuropsychologist

The Computational Neuroscience online course is designed for students interested in Neural and Cognitive Sciences and AI. This course is also beneficial for:

Admission Details

Follow these steps to join the Computational Neuroscience classes:

Step 1: Browse the URL below:

https://onlinecourses.nptel.ac.in/noc23_bt64/preview

Step 2: Click on the “Sign-in/ Register” button

Step 3: Fill out the necessary details and submit the form

The Syllabus

Neuron structure
Networks of Neurons and Synapses
System of neural processing
Basic structures in the brain
Sensory - Executive - Behavior systems

Membrane Potential and All or None Spike
Patch Clamp Techniques, Membrane Potential
Ion Channels
Current Injection - Synapses
Single neuron activity

Point and Compartmental Models of Neurons
Hodgkin Huxley Equations - I
Hodgkin Huxley Equations - II
Reducing the HHE and Moris-Lecar Equations (MLE) 5) Properties of MLE

Phase Plane Analysis - I
Phase Plane Analysis - II
Analyzing HHE
Bifurcations
Other Point Models

Random Variables and Random Processes
Spike Train Statistics and Response Measure
Receptive Fields and Models of Receptive Fields
The Spike Triggered Average (Coding)
Stimulus Reconstruction (Decoding)

Nonlinear approaches: Basics of Information Theory
Maximally Informative Dimensions
Discrimination based approaches
Measuring Spike Train Distances
Statistical Methods in Discrimination

Examples-I: Encoding/Decoding in Neural Systems
Examples-II: Encoding/Decoding in Neural Systems
Neural Population Based Encoding/Decoding - I
Neural Population Based Encoding/Decoding - II
Examples: Population Based Encoding/Decoding

Synaptic Transmission and Synaptic Strength
Ways of Modification of Synaptic Strength
Types of Plasticity
Short-Term Plasticity - I
Short-Term Plasticity - II

Implications of Short-Term Plasticity
Long-Term Plasticity - I
Long-Term Plasticity - II
Modeling Long-Term Plasticity
Computational Implications

Adaptation
Attention
Learning and Memory - I
Learning and Memory - II
Developmental Changes

Conditioning and Reinforcement Learning
Reward Prediction (Error)
Decision Problems
Learning and Memory - II
Developmental Changes

Optimal Coding Principles - I
Optimal Coding Principles - II
Theoretical Approaches to Understanding Plasticity
Current Topics - I
Current Topics - II

Evaluation process

The Computational Neuroscience certification offers an optional exam. Your final score is determined by averaging the best 8 out of 12 assignments (25%) and your proctored exam score (75%).

Instructors

IIT Kharagpur Frequently Asked Questions (FAQ's)

1: What background is required to enroll in the Computational Neuroscience online course?

A prerequisite for this course is the completion of first-year college-level courses in Mathematics and Biology.

2: What topics are covered in the Computational Neuroscience certification syllabus?

The course covers fundamental aspects of computational neuroscience, including quantifying neuronal activity, modeling spiking, understanding information representation by neurons, and implementing plasticity.

3: Who is the target audience for the Computational Neuroscience online course?

The course is intended for students interested in Neural and Cognitive Sciences and AI, providing valuable insights into the computational aspects of brain function.

4: What practical skills will I gain from the Computational Neuroscience training?

You will develop skills in quantifying neuronal activity, computational modeling, and plasticity analysis, enabling you to address complex questions related to learning, memory, and cognition.

5: How does the Computational Neuroscience certification benefit individuals pursuing careers in AI?

The course provides a solid foundation for careers in AI by offering insights into neural computation and its applications, making graduates well-equipped for roles such as Computational Neuroscientist, Neural Data Analyst, or AI Researcher.